README.md

---
library_name: transformers
tags:
- inclusion
license: mit
datasets:
- gikebe/inclusion-dataset
base_model:
- google-bert/bert-base-uncased
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Gikebe
- **Model type:** BERT-based sequence classification model for inclusion-related text classification
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** bert-base-uncased

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://huggingface.co/gikebe/inclusion-model/

## Uses
### Direct Use

This model can be used to classify text related to inclusion, diversity, and social justice topics into different categories such as "Inclusion Mindset," "Intersectionality," "Empowerment," "Privilege," and "Perfectionism."


### Out-of-Scope Use

The model is not suitable for:

-Classification tasks outside of the diversity and inclusion domain.
-Use cases where highly nuanced or sensitive topics require additional layers of ethical consideration.


## Bias, Risks, and Limitations

-The model may reflect biases present in the underlying training data.
-As it is trained on a specific set of texts, it may not generalize well to all contexts related to inclusion and diversity.
-The model could misinterpret or misclassify content in languages other than English or in cultural contexts it wasn’t trained on.


### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```
from transformers import pipeline

classifier = pipeline("text-classification", model="gikebe/inclusion-dataset")
result = classifier("Women of color face unique challenges that are often overlooked in diversity discussions.")
print(result)
```

## Training Details

### Training Data

The model was trained on a custom dataset of inclusion-related texts, derived from books such as Inclusion on Purpose by Ruchika Tulshyan, The Memo by Minda Harts, and others.


### Training Procedure

The training procedure involved fine-tuning the BERT-based model (bert-base-uncased) for text classification.

#### Preprocessing

Text was tokenized using the BertTokenizer with maximum sequence length truncation.


#### Training Hyperparameters

-Epochs: 3
-Batch size: 8
-Optimizer: AdamW
-Learning rate: 5e-5


## Evaluation

The model was evaluated on the same dataset split into training and testing sets.


### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

Relevant factors include the context and specificity of the inclusion-related texts.


#### Metrics

Evaluation is yet to be done


### Results

Evaluation is yet to be done

#### Summary


## Model Examination 

This model is based on the BERT architecture and fine-tuned for sequence classification with the objective of predicting categories related to inclusion and diversity.


## Technical Specifications 
The model was trained using cloud-based GPU resources.


## Citation

**BibTeX:**

```
@misc{gikebe_inclusion_2024,
  author = {Gikebe, [Your Name]},
  title = {Inclusion Model},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/gikebe/inclusion-dataset}},
}
```

**APA:**

Gikebe. (2024). Inclusion Model. Hugging Face. Retrieved from https://huggingface.co/gikebe/inclusion-dataset